Investigation on Dynamic Speech Emotion from the Perspective of Brain Associative Memory

AbstractMany researchers have studied speech emotion for years from the perspective of psychology to engineering. To date, none has made the speech emotion recognition system intuitive enough in such a way that it can be embedded in automatic answering machines that can effectively detect the various affective states of human verbal communication. In most cases the underlying emotional information was misinterpreted thus resulting in wrong feedbacks and responses. The complexity of understanding and analyzing speech emotion is presented in the dynamics of the emotion itself. Emotion is dynamic and changeable over time. Hence, it is imperative to cater for this parameter to boost the performance of the speech emotion recognition system. In this paper, values of Valence (V) and Arousal(A) are used to generate a recalibrated affective space model. Such approach is adopted from psychologists’ understanding that emotion can be represented using emotion primitives’ values. The VA approach is then coupled with the brain associative memory concept that can provides a better means in understanding the dynamics of speech emotion. Results of such analysis tallies with the psychological findings and has its practical implementation

Brain developmental disorders’ modelling based on preschoolers neuro-physiological profiling

Frequently misunderstood by their teachers as being low performers, children with learning disabilities (LDs) such as dyslexia, ADHD, and Asperger’s Syndrome develop low self-confidence and poor self-esteem that may lead to the risk of developing psychological and emotional problems. On contrary, research has shown that a substantial number of these children are capable of learning, and hence, are high-functioning. Therefore, there is a need to provide for the early detection of LDs and instruction that focuses on their needs based on their profiles. Profiling is normally done through observations on the psychological manifestations of LDs by parents and teachers as third-party observers. The first party experience, which is reflected through brain manifestations, is often overlooked. Hence the aim of this paper is to present an alternative solution to profile young children with LDs using electroencephalogram (EEG) that capture brain signals to measure brain functionalities and correlate them with the different LDs. Studies on neurophysiological signals and their relationship to LDs are used to develop Computational Neuro-Physiological (CN-P) model to be an alternative in quantifying the children brain activation function related to learning experience. It is envisaged that such model can profile children with learning disabilities to provide effective intervention in timely manner which can help teachers to provide differentiated instruction for children with LDs. This is in line with the thrust of the Education National Key Result Area (NKRA), the Malaysia Education Blueprint 2013-2025, and the Special Education Regulations 2013

Neuro-physiological porn addiction detection using machine learning approach

Pornography is a portrayal of sexual subject contents for the exclusive purpose of sexual arousal that can lead to addiction. The Internet accessibility has created unprecedented opportunities for sexual education, learning, and growth. Hence, the risk of porn addiction developed by teenagers has also increased due to highly prevalent porn consumption. To date, the only available means of detecting porn addiction is through questionnaire. However, while answering the questions, participants may suppress or exaggerate their answers because porn addiction is considered taboo in the community. Hence, the purpose of this project is to develop an engine with multiple classifiers to recognize porn addiction using electroencephalography signals and to compare classifiers performance. In this work, three different classifiers of Multilayer Perceptron, Naive Bayesian, and Random Forest are employed. The experimental results show that the MLP classifier yielded slightly better accuracy compared to Naïve Bayes and Random Forest classifiers making the MLP classifier preferable for porn addiction recognition. Although this work is still at infancy stage, it is envisaged for the work to be expanded for comprehensive porn addiction recognition system so that early intervention and appropriate support can be given for the teenagers with pornography addiction problem. Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved

Classification of dyslexic and normal children during resting condition using KDE and MLP

Dyslexia is a specific reading disability. It can be characterized by a severe difficulty in reading, learning, spelling, memorizing as well as sequencing activities. In this work, the participants' electroencephalogram (EEG) signals were monitored during resting situation. These signals are captured from the scalp of each subject to measure the brain activities during both eyes opened and eye closed scenarios. Features from the EEG signals were extracted using the Kernel Density Estimation (KDE) and classified using the Multilayer Perceptron (MLP). Due to the large number of features extracted, relevant features are then selected by grouping various spectral components and eliminating irrelevant features. For a comparison purpose, brain signals of three children who are diagnosed of having dyslexia by medical practitioners (denoted as dyslexic) and the other three children diagnosed otherwise (denoted as normal) are used. Experimental results shown that there is a clear distinction between dyslexic and normal children during both eyes closed and eyes opened scenario. Hence, further works can be extended for early intervention in such a way that these children can be further assisted to cope with their learning experience

Extracting features using computational cerebellar model for emotion classification

Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions. Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics. Hence, the features are difficult to be interpreted and yield low classification performance. In this study, a new feature extraction technique using Cerebellar Model Articulation Controller (CMAC) is proposed. The features are extracted from the weights of datadriven self-organizing feature map that are adjusted during training to optimize the error obtained from the desired output and the calculated output. Multi-Layer Perceptron (MLP) classifier is then employed to perform classification on fear, happiness, sadness and calm emotions. Experimental results show that the average accuracy of classifying emotions from EEG signals captured on 12 children aged between 4 to 6 years old ranging from 84.18% to 89.29%. In addition, classification performance for features derived from other techniques such as Power Spectrum Density (PSD), Kernel Density Estimation (KDE) and Mel-Frequency Cepstral Coefficients (MFCC) are also presented as a standard benchmark for comparison purpose. It is observed that the proposed approach is able to yield accuracy of 33.77% to 55% as compared to the respective comparison features. The experimental results indicated that the proposed approach has potential for comparative emotion recognition accuracy when coupled with MLP

Effective tocotrienol dosage traceability system using blockchain technology

Tocotrienol dosage, especially in vitamin E, is important for treatment and prevention of diseases. To date, the dosage is given based on the physician's knowledge and experience to suit the patient’s needs. The alteration of the dosage is depending on the way the patient’s body reaction and coping mechanism which is different from one to another. Hence, the optimal dosage is very difficult to achieve and may result in undesirable side effects. An alternative solution using blockchain technology to trace and chart the dosage of tocotrienol is proposed to capture the effective measure for the patient. With the advancement of the internet of things (IoT) and big data analytics technologies, an effective tocotrienol dosage is possible by utilizing the data gathered from the individual patient for tocotrienol dosage personalization profiling. Then, the output can be used to assist the physician to diagnose an appropriate amount of tocotrienol dosage for optimum effect. This paper discusses the theoretical framework of using blockchain technology to develop an effective tocotrienol dosage traceability system. It is envisaged that such an approach can be a guide to the health practitioners to administer the correct dosage for the patient and subsequently leads to a better quality of life

Emulating human cognitive approach for speech emotion recognition using MLP and GenSoFNN

Speech emotion recognition field is growing due to the increasing needs for effective human-computer interaction. There are many approaches in term of features extraction methods coupled with classifiers to obtain optimum performance. However, none can claim superiority as it is very data-dependant and domain oriented. In this paper, the appropriate sets of features are investigated using segregation method and feature ranking algorithm of Automatic Relevance Determination (ARD) [1]. Two popular classifiers of Multi Layer Perceptron (MLP) [2] and Generic Self-organizing Fuzzy Neural Network (GenSoFNN) [3] are employed to discriminate emotions in the data corpus used in the FAU Aibo Emotion Corpus [4, 5]. The experimental results shows that Mel Frequency Cepstral Coefficient (MFCC) [6] features are able to yield comparable accuracy with baseline result [5]. In addition, it is observed that MLP can perform slightly better than GenSoFNN. Hence, such system envisages that appropriate combination of features extracted with good classifiers is fundamental for the good speech emotion recognition system

EEG affective modelling for dysphoria understanding

Dysphoria is a state of dissatisfaction, restlessness or fidgeting. It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress. To date, 21-item instruments of Depression, Anxiety and Stress Scale (DASS) is employed to measure dysphoria. Although DASS provides a quantitative assessment of the human affective state, it is subjected to interpretation. To complicate matters, pre-cursor emotion and pre-emotion of the participants can result in biasness of the DASS report. Hence, a more direct method in measuring human affective state by analyzing the brain pattern is proposed. The approach can also address the dynamic affective state which is needed in detecting dysphoria. Brain waves pattern are collected using the electroencephalogram (EEG) device and used as the input to analyze the underlying emotion. In this paper, relevant features were extracted using Mel-frequency cepstral coefficients (MFCC) and classified with Multi-Layer Perceptron (MLP). The experimental results show potential of differentiating between positive and negative emotion with comparable accuracy. Subsequently, it is envisaged that the proposed model can be extended as a tool that can be used to measure stress and anxiety in work places and education institutions

Dysphoria detection using EEG signals

Dysphoria is a state faced when one experienced disappointment. If it is not handled properly, dysphoria may trigger acute stress, anxiety and depression. Typically, the individual who experienced dysphoria are in-denial because dysphoria is always being associated with negative connotations such as incompetency to handle pressure, weak personality and lack of will power. To date, there is no accurate instrument to measure dysphoria except using questionnaire by psychologists, such as: Depression, Anxiety and Stress Scale (DASS) and Nepean Dysphoria Scale (NDS-24). Participants may suppress or exaggerate their answers resulting in misdiagnosis. In this work, a theoretical Dysphoria Model of Affect (DMoA) is developed for dysphoria detection. Based on the hypothesis that dysphoria is related to negative emotion, the input from brain signal is captured using electroencephalogram (EEG) device to detect negative emotions. The results from analyzing the EEG signals were compared with DASS and NDS questionnaires for correlation analysis. It is observed that the proposed DMoA approach can identify negative emotions ranging from 55% to 77% accuracy. In addition, the NDS questionnaire seems to provide better distinction for dysphoria as compared to DASS and is similar to the result yielded by DMoA in detecting dysphoria. Thus, DMoA approach can be used as an alternative for early dysphoria detection to assist early intervention in identifying the patients’ mental states. Subsequently, DMoA approach can be implemented as another possible solution for early detection of dysphoria thus providing an enhancement to the present NDS instruments providing psychologists and psychiatrists with a quantitative tool for better analysis of the patients’ state

Enhancing Talent Development Using AI-Driven Curriculum-Industry Integration

The specific hiring needs render low-skill-based job-seeking invalid in coping with the nation's economic development. There needs to be more graduate readiness for the industry's needs. This paper explores the transformative potential of Artificial Intelligence (AI) in fostering a symbiotic relationship between academic curricula and industry demands, aimed at building a robust talent pool for the future. A new hiring selection model that matches industry-identified hiring parameters with the knowledge and skills obtained from the university. By aligning educational programs with real-world challenges and market needs, this novel approach seeks to propel the growth of talents